Cross-Language Speech Dubbing Using Speaker Feature Transfer
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Solution Overview
Problem
The manual translation and dubbing of audio in video content across languages is costly and inefficient, lacking an effective automated solution to maintain an approximate or consistent presentation effect with the original audio.
Innovation Solution
An automated method and apparatus that processes speech content by determining first speech content, generating corresponding text in a second language, and synthesizing speech content based on speech and text feature representations to create a second video with aligned audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual translation and dubbing is used, then quality and accuracy are ensured, but cost increases and working efficiency decreases
Solution Approach 1:
The patent segments the dubbing process into distinct modules: speech content identification, speech feature extraction, text translation, and speech synthesis. Each module handles a specific task, allowing automated processing while maintaining quality control at each stage.
Solution Approach 2:
The patent introduces text as an intermediary element between source speech and target speech. The source speech is converted to text, translated to target language text, then synthesized back to speech, enabling automated processing while preserving translation quality.
2Manufacturing precision
If manual translation and dubbing is used, then translation quality is ensured, but cost increases
Solution Approach 1:
The system performs self-service by automatically extracting speech features, translating text, and synthesizing target speech without requiring human operators for each dubbing task, significantly reducing labor costs while maintaining quality through automated quality control mechanisms.
Solution Approach 2:
The patent changes the processing parameters from manual human operations to automated computational processes, transforming the cost structure from labor-intensive to computation-intensive, thereby reducing overall cost while maintaining or improving quality consistency.
3Productivity
If automated speech processing is implemented, then working efficiency improves, but automation degree increases
Solution Approach 1:
By dividing the dubbing process into segmented modules (speech identification, feature extraction, translation, synthesis), the system achieves high automation in each module while maintaining overall process controllability and quality assurance.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computational systems, substituting human speech processing with algorithm-based speech recognition, translation, and synthesis, thereby achieving high working efficiency through increased automation.
4Productivity
If automated speech processing is implemented, then working efficiency improves, but cost structure changes
Solution Approach 1:
The automated system performs dubbing operations independently without requiring continuous human intervention, reducing labor costs significantly. The system serves itself by automatically processing speech content through the entire pipeline from identification to synthesis.
Solution Approach 2:
The patent fundamentally changes the cost parameters by replacing labor cost with computational resource cost, transforming the economic model from manual service to automated processing, thereby improving working efficiency while optimizing the overall cost structure.
Data Source
AI summary
A method, an apparatus, a device, and a storage medium for processing speech content are provided. First speech content associated with a target object from target speech content is determined, and the first speech content corresponding to the first text. A second text corresponding to the first text is generated, the first text corresponds to a first language, and the second text corresponds to a second language. Based on at least one segment of the target speech content associated with the target object, a speech feature representation corresponding to the target object is determined. Based on the speech feature representation and a text feature representation of the second text, second speech content corresponding to the second text is generated.